2d_sat.py

Usage: 2d_sat.py [OPTIONS] IN_FILE
Reads data from IN_FILE with the following format:
comment_tree_id, commenter_id, voter_id, vote_type
1, 200, 3000, 1
1, 201, 3000, -1
...
or (in case of real data):
r.reply_to r.message_id r.uid_alias r.vote_type
1 200 3000 UP
1 201 3000 DOWN
...
Outputs a CSV which contains whether the comment/article-tree had a sign-
rank of 2 or not.
Redirect output to a file to save it.
Options:
--dims INTEGER What dimensional embedding to test for?
--cpus INTEGER How many CPUs to use.
--timeout INTEGER Time after which to give up (ms).
--real / --no-real Assume format of real-data.
--improve PATH Improve the results from the provided file.
Will only run for `unknown` ids in the file.
--context-id / --no-context-id Use the context_id instead of
comment_tree_id to group comments into
matrices.
--nrows INTEGER Number of rows from the CSV to read.
--help Show this message and exit.

SR_fill.py

Usage: SR_fill.py [OPTIONS] IN_MAT_FILE OP_MAT_FILE OP_SC_FILE
Read M_partial from IN_MAT_FILE and fill in the matrix using SR. The vote
at (i, j) will be removed before filling in the matrix. The resulting
matrix will be saved at OP_MAT_FILE and the given LOO entry will be placed
along with the original vote in OP_LOO_FILE.
Additionally, the best guess for the Sign Rank will be placed in
OP_SC_FILE along with the source node which results in that.
Options:
-i INTEGER LOO i
-j INTEGER LOO j
--op-loo PATH Output path for the LOO.
--seed INTEGER Seed which was used to create this test-case.
--min-avg / --no-min-avg This flag will cause minimization of the
average SC instead of worst case SC. Is much
faster.
--transpose / --no-transpose Whether to transpose the matrix or not.
--help Show this message and exit.

z3_fill.py

Usage: z3_fill.py [OPTIONS] IN_MAT_FILE OP_MAT_FILE OP_LOO_FILE
Read M_partial from IN_MAT_FILE and fill in the matrix using Z3. The vote
at (i, j) will be removed before filling in the matrix. The resulting
matrix will be saved at OP_MAT_FILE and the given LOO entry will be placed
along with the original vote in OP_LOO_FILE.
Options:
-i INTEGER LOO i
-j INTEGER LOO j
--sat_2d TEXT Is the matrix 2D-SAT w/o LOO?
--sat_1d TEXT Is the matrix 1D-SAT w/o LOO?
--seed INTEGER Seed which was used to create this test-cast.
--guess INTEGER Whether to use the given guess {-1, +1} for doing LOO
prediction; 0 means no guessing.
--help Show this message and exit.

low-rank-completion.py

Usage: low-rank-completion.py [OPTIONS] IN_MAT_FILE
Read M_partial from IN_MAT_FILE and optimize the embeddings to maximize
the likelihood under the logit model.
Options:
--dims INTEGER The dimensionality of the embedding.
--seed INTEGER The random seed to use for initializing
matrices, in case initial values are not given.
--suffix TEXT Suffix to add before saving the embeddings.
--init-c-vecs TEXT File which contains initial embedding of c_vecs.
--init-v-vecs TEXT File which contains initial embedding of v_vecs.
-i INTEGER Which i index to LOO.
-j INTEGER Which j index to LOO.
--alpha FLOAT Bound on the spikiness of M.
--sigma FLOAT What is the variance of (logistic) noise to add.
--lbfgs / --no-lbfgs Whether to use LBFGS instead of BFGS.
--loo-output TEXT Where to save the LOO output.
--loo-only / --no-loo-only Whether to only save the LOO output or whether
to save the complete recovered matrix.
--uv / --no-uv Whether to impose the alpha constraint on both U
and V or on U.V^T.
--verbose / --no-verbose Verbose output.
--help Show this message and exit.

soft-impute.py

Usage: soft-impute.py [OPTIONS] IN_MAT_FILE
Read M_partial from IN_MAT_FILE and complete the matrix using soft-impute
method.
Options:
--dims INTEGER The dimensionality of the embedding.
--seed INTEGER The random seed to use for initializing
matrices, in case initial values are not given.
--suffix TEXT Suffix to add before saving the embeddings.
-i INTEGER Which i index to LOO.
-j INTEGER Which j index to LOO.
--loo-output TEXT Where to save the LOO output.
--loo-only / --no-loo-only Whether to only save the LOO output or whether
to save the complete recovered matrix.
--verbose / --no-verbose Verbose output.
--help Show this message and exit.

matrix-Z3-embed.py

Usage: matrix-Z3-embed.py [OPTIONS] MAT_FILE
Read the partial matrix in MAT_FILE and save embeddings for the file to
`mat_file.commenters` and `mat_file.voters` file.
Options:
--dim INTEGER What dimension to use while splitting matrix.
--timeout INTEGER What timeout to use (minutes).
--help Show this message and exit.

Requirements

These python packages are required:

click

cvxpy

dccp

numpy

decorated_options

seaborn

z3-solver

networkx

pqdict

fancyimpute

All of these can be installed using pip while some of them are available on
conda (preferred). If using pip, the requirements.txt file in the code
folder will be helpful.